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One of the key challenges in Multi-Spectral Automatic Diagnostic (MAD) robot design is the precise targeting of narrow-angle cameras on a specific part of the equipment. The paper shows that a low-cost MAD robot, whose navigation system is based on open-source ArduRover firmware and a pair of low-cost Ublox F9P GNSS receivers, can inspect the 8 × 4 degree ultraviolet camera bounding the targeting error within 0.5 degrees. To achieve this result, we propose a new targeting procedure that can be implemented without any modifications in ArduRover firmware and outperforms more expensive solutions based on LiDAR SLAM and UWB. This paper will be interesting to the developers of robotic systems for power equipment inspection because it proposes a simple and effective solution for MAD robots’ camera targeting and provides the first quantitative analysis of the GNSS reception conditions during power equipment inspection. This analysis is based on the experimental results collected during the inspection of the overhead power transmission lines and equipment inspections on the open switchgear of different power plants. Moreover, it includes not only satellite, dilution of precision, and positioning/heading estimation accuracy but also the direct measurements of angular errors that could be achieved on operating power plants using GNSS-only camera targeting.

Ensuring high-quality and uninterrupted power supply to consumers is one of the main problems of creating reliable power systems of a new generation. It is associated with the implementation of an integral assessment of the technical state of equipment of the power stations and substations, based on technical diagnostics data. Integral assessment involves the choice of ranges of the set of parameters of the technical state for groups of constituent elements of equipment, as well as the determination of their weight coefficients. Currently, the problem is solved with the help of expert assessments, arbitrarily in each specific case, which may lead to an incorrect integral assessment of the state of the equipment. The principle of decomposition makes it possible to determine the individual performance characteristics of each of them. At the same time, their subsequent aggregation ensures that the emergent properties of the system are taken into account. Such an approach was used in this work to evaluate individual types of equipment and their constituent elements. The algorithm for constructing a tree with a minimum random weight, proposed in this paper, makes it possible to increase the validity of decisions. They are made at various stages of designing complex technical systems and include tasks with an integral assessment of the technical state of equipment of power plants and substations. In the proposed algorithm, as a result of using the tree of variants, a matroid is formed, on which, using the “greedy” algorithm, the optimal solution can be determined.

Modern electrical grippers have lower life-cycle costs compared to pneumatic ones. Furthermore, they provide force control, making it possible to grasp objects with different fragility using a single device. At the same time, electrical grippers have a higher end-effector weight, installed on the robot’s flange and lower closing speed, preventing them from replacing pneumatic solutions in high dynamic Pick and Place applications. This research faces both issues by synthesizing a novel gripper mechanism based on a Torque Distribution Gearbox, which makes it possible to relocate the electric motors to the static frame of a delta robot. The proposed gripper not only has a lower mass and a higher closing speed than competitive electric solutions, but it also provides unlimited rotation around the vertical axis. The performance of the gripper was tested in experimental studies, which showed that a created aluminum prototype provides a precise force control in the range from 3 N to 48 N with an accuracy not worse than 1.27 N. Moreover, its finger’s speed is 3.1–56 times higher than market available electrical grippers, which makes it comparable by this parameter with pneumatic solutions used in high dynamic Pick and Place applications.

This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects’ observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers’ technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F1-score, was estimated to be 83%.

The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.